Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87508
Authors: 
Croux, Christophe
Exterkate, Peter
Year of Publication: 
2011
Series/Report no.: 
Tinbergen Institute Discussion Paper 11-122/4
Abstract: 
Factor construction methods are widely used to summarize a large panel of variables by means of a relatively small number of representative factors. We propose a novel factor construction procedure that enjoys the properties of robustness to outliers and of sparsity; that is, having relatively few nonzero factor loadings. Compared to more traditional factor construction methods, we find that this procedure leads to better interpretable factors and to a favorable forecasting performance, both in a Monte Carlo experiment and in two empirical applications to large data sets, one from macroeconomics and one from microeconomics.
Subjects: 
dimension reduction
forecasting
outliers
regularization
JEL: 
C38
C51
C53
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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